Perspectives on endogenous and exogenous tissue engineering following injury to tissues of the knee
Bibliographic record
Abstract
The knee is a multi-component organ system comprised of several tissues which function coordinately to provide mobility. Injury to any one component compromises the integrity of the system and leads to adaptation of the other components. Over time, such events often lead to dysfunction and degeneration of the knee. Therefore, there has been considerable research emphasis to repair injured components in the knee including cartilage, menisci, and ligaments. Approaches to improving healing and repair/regeneration of knee tissues have included surgery, anti-sense gene therapy, injection of growth factors and inflammatory cytokine antagonists, transplantation of in vitro expanded chondrocytes, enhancement of endogenous cells via microfracture, injection of mesenchymal stem cells, and implantation of in vitro tissue engineered constructs. Some of these approaches have lead to temporary improvement in knee functioning, while others offer the potential to restore function and tissue integrity for longer periods of time. This article will review the status of many of these approaches, and provide a perspective on their limitations and potential to contribute to restoration of knee function across the lifespan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".